{"id":"W2920914266","doi":"10.1111/cge.13531","title":"Genetic counselors' preferences for coverage of preimplantation genetic diagnosis: A discrete choice experiment","year":2019,"lang":"en","type":"article","venue":"Clinical Genetics","topic":"Prenatal Screening and Diagnostics","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; SickKids Foundation; Hospital for Sick Children; Institute for Clinical Evaluative Sciences; University of Calgary; Trillium Health Centre","funders":"Institute of Health Services and Policy Research; Canadian Institutes of Health Research; Hospital for Sick Children","keywords":"Multinomial logistic regression; Genetic testing; Logistic regression; Scope (computer science); Genetic counseling; Actuarial science; Preimplantation genetic diagnosis; Fertility; Mixed logit; Discrete choice; Family history; Psychology; Medicine; Demography; Environmental health; Business; Econometrics; Computer science; Economics; Biology; Population; Genetics; Pregnancy; Surgery","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000212964,0.0002000492,0.0005218553,0.00004702385,0.00003597454,0.0000209825,0.0001910788,0.0002133297,0.00009320452],"category_scores_gemma":[0.0007899776,0.0001708464,0.0002096948,0.00009962448,0.0001211187,0.00002793381,0.00006819928,0.0001591628,0.00004608144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000207665,"about_ca_system_score_gemma":0.0001423568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002785059,"about_ca_topic_score_gemma":0.00001215369,"domain_scores_codex":[0.9979537,0.0000849204,0.0008894426,0.0004356075,0.0003397712,0.000296569],"domain_scores_gemma":[0.9967053,0.002111515,0.0002701975,0.000493022,0.0001841172,0.0002358709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003482548,0.00035085,0.9625963,0.0002721026,0.0001241869,0.000004761272,0.0001366912,0.0006054029,0.0004732009,0.00002303774,0.0009509224,0.0341143],"study_design_scores_gemma":[0.003341214,0.003767976,0.9772183,0.0003121099,0.0003484418,0.00001132183,0.00005408091,0.002454648,0.005815557,0.0004231159,0.006015486,0.0002377249],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885135,0.003358964,0.005457224,0.0000746579,0.0005229364,0.001404662,0.0001253928,0.0000407676,0.0005018403],"genre_scores_gemma":[0.9783046,0.004727853,0.01581676,0.0003263271,0.0003464115,0.0001975302,0.00007738731,0.00003295431,0.0001701506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03387658,"threshold_uncertainty_score":0.6966915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06029169278726957,"score_gpt":0.3835990666178723,"score_spread":0.3233073738306027,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}